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Models/CLIP VIT-B16

CLIP VIT-B16

Reported on 6 benchmarks across 6 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology4 results

  • 3DonOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020
  • 2D ClassificationonOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020
  • 2D Object DetectiononOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020
  • 16konOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020

Computer Vision2 results

  • Object DetectiononOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020
  • Open Vocabulary Object DetectiononOVAD-Box benchmark
    mean average precision· uses extra data· 2021-02-26
    16.6
    best: 28 (X-VLM)
    SOTA
    Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020